Terms of Service for an AI Product

A copied SaaS template covers your subscription, your liability cap, and your right to terminate. It says nothing about who owns what your product generates, whether you promise the output is correct, what happens when you swap the underlying model, or what customers are allowed to feed in. Those four gaps are where disputes with AI products actually start.

Terms of service for an AI product therefore need four clauses that ordinary software terms do not contain. This is not legal advice, and the clauses below are not drafting language. They are the four questions your terms have to answer before a lawyer can do anything useful with them.

Clause one: who owns the output

Your users need to know whether they can use what your product produces, commercially, without asking you. If your terms are silent, they will assume they can, and one of them will eventually find out you disagree at the worst possible moment.

Three things to settle explicitly:

• Assignment or licence. Most products assign output rights to the user, or grant a broad perpetual licence. Silence is the worst option because it invites the argument.

• Your own licence back. If you need to retain rights to output for abuse monitoring, support, or improving the product, say so here rather than burying it elsewhere.

• The honest caveat. Similar prompts can produce similar output for different users, and copyright in machine-generated material is unsettled in many jurisdictions. Terms that promise exclusivity in output are promising something you cannot deliver.

The related question of who owns model weights or fine-tunes is separate and belongs in its own clause if it applies to you.

Clause two: no warranty of accuracy, stated where people see it

Every generative product produces wrong output sometimes. Your terms need to say that plainly, and the disclaimer needs to be specific rather than a generic "as is" clause inherited from a template.

Specific means naming the behaviour: the service uses machine learning models whose output may be inaccurate, incomplete, or unsuitable for a particular purpose, and the user is responsible for reviewing output before relying on it.

Two practical points. First, a disclaimer buried in clause 14.3 does less work than the same statement shown in the interface at the point of output. Courts and regulators in several jurisdictions look at whether a term was reasonably brought to the user's attention. Second, disclaimers of this kind have limits against consumers in many places, which is one of several reasons a business-to-business product and a consumer product should not share the same terms.

Clause three: the right to change models

This is the clause almost nobody includes and almost everybody needs. Your product depends on a model you do not control. That model will be deprecated, repriced, or changed in behaviour, on the provider's schedule rather than yours.

Your terms should establish:

Without the first row, a customer on an annual contract can argue that the product they bought is not the product they now have. With it, you have a defensible position and, more usefully, a clear expectation set up front.

Pair this with your uptime and support commitments rather than mixing them, which is the domain of writing an SLA for an AI product.

Clause four: what users may put in

Input restrictions protect you from a problem your users create. Someone will paste a customer database, a patient record, or third-party confidential material into your product, and if your terms are silent about it, the resulting mess is partly yours.

State that users must have the right to submit what they submit, must not submit special-category personal data unless you have specifically built for it, and are responsible for obtaining any consents their own use requires.

Note that this clause and your acceptable use policy do different jobs. Input restrictions are about the legality of the data. Acceptable use is about what people may do with the product, which is the subject of drafting an acceptable use policy. Keep them separate documents and reference one from the other.

Where the regulation touches this

If you serve EU users, the EU AI Act creates transparency duties that vary by the role you play and the risk category of your system. Terms of service are not where compliance happens, but they are where several of the disclosures naturally land, particularly around informing users they are interacting with an AI system and about the nature of generated content.

Check which obligations apply to your specific product rather than assuming. The categories are narrower than the coverage suggests, and getting this wrong in either direction is expensive: unnecessary compliance work, or a gap you did not know you had.

What you can draft and what you cannot

Realistically, you can write the first draft of all four clauses yourself, and you should, because you know the product and a lawyer does not. What you cannot do is decide which jurisdiction's consumer law applies to your user base, whether your liability cap is enforceable where your customers are, or whether your output-ownership language survives contact with local copyright rules.

A sensible sequence:

1. Draft the four clauses above in plain language, saying what you actually intend.

2. Write down your real risk exposure: what is the worst plausible claim, and from whom.

3. Take both to a lawyer who works with software companies. Reviewing a specific draft is far cheaper than commissioning terms from nothing.

4. Keep your terms, privacy policy, acceptable use policy, and SLA as separate documents that reference each other. Merging them produces something nobody reads and that is harder to update.

Point four matters more than it sounds. Your privacy policy has a different audience and a different legal basis, and it needs its own treatment, as in writing a privacy policy for an AI app. Terms bundled into one enormous page get updated less often, and stale terms are worse than short ones. The commercial framing around all of this sits in the wider set of AI monetization strategies.

FAQ

Can I just use a terms of service generator?

As a starting skeleton, yes. Generators produce standard SaaS terms and will not cover output ownership, accuracy disclaimers, or model-change rights, which are exactly the clauses that matter. Terms of service for an AI product need those four additions on top of whatever a generator gives you.

Do I need different terms for business and consumer customers?

Usually yes. Consumer protection law limits what you can disclaim and how you can change terms, in ways that do not apply between businesses. Running one set of terms across both generally means accepting the stricter constraints everywhere.

What happens if my terms say nothing about output ownership?

The default depends on jurisdiction and on how your interface is worded, which is a slow and expensive way to find out. Users typically assume they own what they generate. If that matches your intention, say so and remove the ambiguity.

How often should I update them?

Whenever the product changes materially, and on any change of underlying provider that alters data handling. Give notice as your terms require, keep a dated version history, and do not silently rewrite them, which damages trust more than the change itself usually does.